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Shared Context -- job-forge

Read these sources of truth

File Path Loaded where?
cv.md cv.md (project root) Already in context via opencode.json:instructions — do NOT Read again
_shared.md modes/_shared.md (this file) Already in context via instructions
AGENTS.harness.md symlink to node_modules/job-forge/AGENTS.md Already in context via instructions
article-digest.md article-digest.md (if exists) Read on demand — optional detailed proof points, only when evaluating
profile.yml config/profile.yml Read on demand — for name, contact, targets
templates/states.yml canonical application states Already in context via instructions

RULE: Do NOT Read cv.md or modes/_shared.md from within a mode — they're already in your context. Re-Reading them pulls the same tokens into conversation history at full input cost; opencode's instructions array caches them for you.

RULE: NEVER hardcode metrics from proof points. Reference them from the already-loaded cv.md, or Read article-digest.md if you need the richer proof points.

RULE: For article/project metrics, article-digest.md takes precedence over cv.md (cv.md may have older numbers).

List the North Star target roles

The skill applies with EQUAL rigor to ALL target roles. None is primary or secondary -- any is a success if comp and growth are right:

Archetype Thematic axes What they buy
AI Platform / LLMOps Engineer Evaluation, observability, reliability, pipelines Someone who puts AI in production with metrics
Agentic Workflows / Automation HITL, tooling, orchestration, multi-agent Someone who builds reliable agent systems
Technical AI Product Manager GenAI/Agents, PRDs, discovery, delivery Someone who translates business to AI product
AI Solutions Architect Hyperautomation, enterprise, integrations Someone who designs end-to-end AI architectures
AI Forward Deployed Engineer Client-facing, fast delivery, prototyping Someone who delivers AI solutions to clients fast
AI Transformation Lead Change management, adoption, org enablement Someone who leads AI transformation in an org

Apply framing by archetype

Concrete metrics: read from cv.md + article-digest.md at evaluation time. NEVER hardcode numbers here.

If the role is... Emphasize about the candidate... Proof point sources
Platform / LLMOps Production systems builder, observability, evals, closed-loop article-digest.md + cv.md
Agentic / Automation Multi-agent orchestration, HITL, reliability, cost article-digest.md + cv.md
Technical AI PM Product discovery, PRDs, metrics, stakeholder mgmt cv.md + article-digest.md
Solutions Architect System design, integrations, enterprise-ready article-digest.md + cv.md
Forward Deployed Engineer Fast delivery, client-facing, prototype to prod cv.md + article-digest.md
AI Transformation Lead Change management, team enablement, adoption cv.md + article-digest.md

Use this exit narrative in ALL framings

Use the candidate's exit story from config/profile.yml to frame ALL content:

  • In PDF Summaries: Bridge from past to future -- "Now applying the same [skill] to [JD domain]."
  • In STAR stories: Reference proof points from article-digest.md
  • In Draft Answers (Section G): Place the transition narrative in the first response.
  • When the JD asks for "entrepreneurial", "ownership", "builder", "end-to-end": This is the #1 differentiator. Increase match weight.

Use the cross-cutting advantage frame

Frame the candidate as a "Technical builder with real-world proof". Adapt the phrasing per archetype.

  • For PM: "builder who reduces uncertainty with prototypes then productionizes with discipline."
  • For FDE: "builder who delivers fast with observability and metrics from day 1."
  • For SA: "builder who designs end-to-end systems with real integration experience."
  • For LLMOps: "builder who puts AI in production with closed-loop quality systems."

Convert "builder" into a professional signal, not a "hobby maker". Real proof points make this credible.

Use the portfolio as a proof point in high-value applications

If the candidate has a live demo/dashboard (check profile.yml), offer access in applications for relevant roles.

Comp Intelligence

Follow this general guidance.

  • Use WebSearch for current market data (Glassdoor, Levels.fyi, Blind).
  • Frame by role title, not by skills — titles determine comp bands.
  • Contractor rates are typically 30-50% higher than employee base to account for benefits.
  • Geographic arbitrage works for remote roles: lower CoL = better net.

Negotiation Scripts

Salary expectations (general framework):

"Based on market data for this role, I'm targeting [RANGE from profile.yml]. I'm flexible on structure -- what matters is the total package and the opportunity."

Geographic discount pushback:

"The roles I'm competitive for are output-based, not location-based. My track record doesn't change based on postal code."

When offered below target:

"I'm comparing with opportunities in the [higher range]. I'm drawn to [company] because of [reason]. Can we explore [target]?"

Location Policy

In forms:

  • Binary "can you be on-site?" questions: follow your actual availability from profile.yml
  • In free-text fields: specify your timezone overlap and availability

In evaluations (scoring):

  • Remote dimension for hybrid outside your country: score 3.0 (not 1.0)
  • Only score 1.0 if JD explicitly says "must be on-site 4-5 days/week, no exceptions"

Use time-to-offer as the priority

  • Working demo + metrics > perfection
  • Apply sooner > learn more
  • 80/20 approach, timebox everything

Use this canonical scoring model (SINGLE SOURCE OF TRUTH)

ALL evaluation modes MUST use this exact model. Whether the offer is evaluated via offer, auto-pipeline, batch, or compared via compare, the score is computed the same way. The executable source of truth is templates/score.json; use npx job-forge score:check --input <score.json> and score:gate --gate apply when validating emitted score JSON instead of recalculating gates in prose. This ensures scores are comparable across the entire pipeline.

# Dimension Weight 1 3 5
1 North Star alignment 25% Unrelated to any target archetype Adjacent — transferable skills apply Exact target archetype match
2 CV match 15% <40% requirements covered 60-75% covered, gaps are soft 90%+ covered with proof points
3 Seniority fit 15% Junior / 2+ levels below Mid-senior, manageable positioning Staff+ or exact level match
4 Comp estimate 10% Well below market / no data Median for role+location Top quartile or above target
5 Growth trajectory 10% Dead end, no progression path 1-2 yr path forward, timeline unclear Clear path to next level
6 Remote quality 5% On-site only, no flexibility Hybrid 2-3 days in office Full remote, async-friendly
7 Company reputation 5% Red flags, poor reviews Average employer, neutral signals Top employer, strong brand
8 Tech stack modernity 5% Legacy, no AI/ML relevance Mix of modern and legacy tooling Frontier AI/ML stack (LLMs, agents, evals)
9 Speed to offer 5% 6+ month process, bureaucratic Standard 4-6 week process Fast-track, <4 weeks typical
10 Cultural signals 5% Bureaucratic, risk-averse Mixed signals Builder culture, high ownership

Final score = weighted sum, rounded to 1 decimal (e.g., 4.2/5).

Score Emission — EMIT-ONCE JSON (REQUIRED)

Before writing any report prose, emit the score as a single JSON block. Then derive the narrative from the JSON — do NOT re-enumerate the 10 dimensions in thinking or prose. This rule exists because repeated scoring monologues are a major token-burn source: a typical eval session otherwise spends ~1,500 tokens narrating the same 10 scores two or three times.

Shape (emit exactly one block, nothing else in the block):

{
  "report_num": "520",
  "company": "Anthropic",
  "role": "Manager, Forward Deployed Engineering",
  "archetype": "AI Forward Deployed Engineer",
  "url": "https://job-boards.greenhouse.io/anthropic/jobs/5099753008",
  "date": "2026-04-15",
  "scores": {
    "north_star":    { "score": 5, "rationale": "Exact FDE archetype match" },
    "cv_match":      { "score": 3, "rationale": "60% coverage; mgmt gap" },
    "seniority_fit": { "score": 3, "rationale": "Senior IC, no formal mgmt" },
    "comp":          { "score": 5, "rationale": "$320-400K vs target $135-200K" },
    "growth":        { "score": 5, "rationale": "Founding team" },
    "remote":        { "score": 3, "rationale": "Hybrid, 25% in-office" },
    "company":       { "score": 5, "rationale": "Top AI brand" },
    "stack":         { "score": 5, "rationale": "Cutting-edge AI" },
    "speed":         { "score": 3, "rationale": "Standard process" },
    "culture":       { "score": 5, "rationale": "Builder culture" }
  },
  "weighted_total": 4.2,
  "recommendation": "apply",
  "pdf_threshold_met": true,
  "draft_answers_threshold_met": true
}

Rules:

  • score per dimension is 1-5 (integer or 0.5 step).
  • rationale is ≤ 80 characters, no markdown.
  • weighted_total = round(Σ weight × score, 1). The 10 weights sum to 100% exactly.
  • recommendation ∈ {"apply" (≥3.5), "apply_with_caveats" (3.0-3.4), "skip" (<3.0)}.
  • pdf_threshold_met ⇔ weighted_total ≥ 3.0.
  • draft_answers_threshold_met ⇔ weighted_total ≥ 3.5.

After emitting the JSON:

  1. Embed the same JSON block verbatim in the report .md under a ## Score section (fenced as ```json).
  2. Save/check the emitted block when it will drive a PDF, application gate, comparison, or batch dispatch: npx job-forge score:check --input <score.json> and, for apply decisions, npx job-forge score:gate --input <score.json> --gate apply.
  3. Write Blocks A-F referencing the scores by key (e.g., "Seniority fit: 3/5 — Senior IC, no formal management"). Do NOT re-list all 10 dimensions in prose. Do NOT repeat the rationales verbatim.
  4. Do NOT narrate the scoring process in thinking before emitting the JSON. Decide, emit, move on.

Score interpretation (use consistently everywhere):

  • 4.5-5.0 — Strong match. Generate PDF + draft answers. Apply promptly.
  • 3.5-4.4 — Apply with tailored CV. Generate PDF + draft answers.
  • 3.0-3.4 — Moderate match. Generate PDF. Flag gaps to candidate before applying.
  • < 3.0 — Weak match. Report only. Explicitly discourage applying unless candidate has a specific reason.

PDF generation threshold: >= 3.0 (consistent across all modes). Draft answer threshold: >= 3.5 (consistent across all modes).

Scoring Calibration Anchors

Moved: the score anchors table now lives in modes/_shared-calibration.md — Read on-demand during evaluation, NOT loaded into the global instructions prefix. This split matters because the anchors churn as you accumulate reports; keeping them out of the cached prefix means each update doesn't bust cache for every unrelated session.

Rule: right before assigning a final score, Read modes/_shared-calibration.md once (per evaluation, not per dimension) and sanity-check your score against the anchors. Do not Read it more than once per evaluation.

Global Rules

Skip these actions

  1. Invent experience or metrics
  2. Modify cv.md or portfolio files
  3. Submit applications on behalf of the candidate
  4. Share phone number in generated messages
  5. Recommend comp below market rate
  6. Generate a PDF without reading the JD first
  7. Use corporate-speak
  8. Ignore the tracker (every evaluated offer gets registered)
  9. Use AI-hallmark words: "leveraged", "utilized", "spearheaded", "orchestrated" (as metaphor), "cutting-edge", "passionate about", "drive innovation", "synergy", "holistic approach", "navigate complex", "foster collaboration". ATS platforms flag these. Use plain, specific verbs instead.

Do these always

  1. Cover letter: If the form has an option to attach or write a cover letter, ALWAYS include one. Generate PDF with the same visual design as the CV. Content: JD quotes mapped to proof points, links to relevant case studies. 1 page max.
  2. Read cv.md and article-digest.md (if exists) before evaluating any offer 1b. First evaluation of each session: Run npx job-forge sync-check with Bash. If it reports warnings, notify the candidate before continuing
  3. Detect the role archetype and adapt framing
  4. Cite exact lines from CV when matching
  5. Use WebSearch for comp and company data
  6. Register in tracker after evaluating
  7. Generate content in the language of the JD (EN default)
  8. Be direct and actionable -- no fluff
  9. When generating English text (PDF summaries, bullets, LinkedIn messages, STAR stories): native tech English, not translated. Short sentences, action verbs, no unnecessary passive voice. 8b. Case study URLs in PDF Professional Summary: If the PDF mentions case studies or demos, URLs MUST appear in the first paragraph (Professional Summary). The recruiter may only read the summary. All URLs with white-space: nowrap in HTML.
  10. Tracker additions as TSV -- NEVER add new entries directly to day files in data/applications/. Write TSV in batch/tracker-additions/ and merge-tracker.mjs handles the merge.
  11. Include **URL:** in every report header -- between Score and PDF.

Use these tools

Tool Use
WebSearch Comp research, trends, company culture, LinkedIn contacts, fallback for JDs
WebFetch Fallback for extracting JDs from static pages
Geometra MCP Verify offers (geometra_page_model), extract JDs (geometra_connect + geometra_snapshot), fill forms (geometra_fill_form), generate PDFs (geometra_generate_pdf). The MCP server accepts up to 5 concurrent sessions, but orchestrator subagent parallelism is capped at 2 (Hard Limit #1 in AGENTS.md) — do not interpret the 5-session ceiling as permission to dispatch 5 subagents. Each geometra_connect returns a sessionId — pass it to all subsequent tool calls to target the right session. Omitting sessionId targets the most recently connected session. Use geometra_list_sessions to inspect active sessions.
Read cv.md, article-digest.md, cv-template.html
Write Temporary HTML for PDF, day files in data/applications/YYYY-MM-DD.md, reports .md
Edit Update tracker
Bash npx job-forge pdf